Build a minimum credible telemetry asset, not a large platform. The repository should prove that timestamped event streams can be transformed into window-level features and alert-oriented outputs.
The design target is a bridge project: strong enough to read like a small monitoring prototype, but narrow enough to stay understandable and runnable for a single developer.
The implementation follows a narrow pipeline:
io.pyloads JSONL or CSV inputs and validates required fields.preprocess.pynormalizes timestamps and categorical values.windowing.pybuilds sliding windows over sorted events.features.pycomputes per-window telemetry features.rules.pyturns features into rule-based alerts.visualize.pyrenders operator-facing PNG outputs.cli.pywires the pipeline intorun,summarize, andplot.
pandasis used for clarity and concise feature computation.- Rules stay threshold-based to emphasize detection semantics over model complexity.
- Relative config paths are resolved from the repository root when the config lives under
configs/. - Outputs are CSV, JSON, and PNG: tables and summaries stay easy to diff, while PNG timelines are easy to inspect and embed in README material.
- The notebook remains intentionally tiny so the packaged CLI pipeline stays the primary entrypoint.
- distributed stream processing
- live ingestion from production systems
- durable storage
- dashboard hosting
- production alert delivery